Here we are analyzed about GSE114260 dataset, detailed information about this dataset is given below.
Platform title: Illumina HiSeq 2000 (Homo sapiens)
Submission data: Nov 02 2010
Last update data: Mar 27 2019
Organism: Homo sapiens
Number of GEO datasets that use this techology : 7897
Number of GEO samples that use this technology : 122103
Refer to corresponding paper, combined in file name means this file contains samples from both human and mice, since we only care about human, don’t need to use the two combined file. Therefore, the stranded_read_counts is the data file of interest.
To clarify, ER in this data set stands for estrogen receptor, not Endoplasmic reticulum.In this dataset, we have 2 groups with 3 replicates each, so 6 samples in total. C4-12ERaERE (ER lacking cell line stably transfected with ERaERE) relative to the MCF7 cells were used for RNAseq analysis. The treatment group undergo paroxetine and estrogen (E2) treatment, while in control group there is only paroxetine treatment.
Raw data has been normalized using trimmed mean approaches. The we performed threshold over-representation analysis, by g:Profiler, on the normalized dataset. The enrichment analysis result support conclusion in the original paper.Top gene returned is ESR1 and top term returned is ‘GO:0006614 SRP-dependent cotranslational protein targeting to membrane’. Refer to gene summary provided by HGNC, ESR1 encodes an estrogen receptor. In addition, there are paper shows that term ‘GO:0006614 SRP-dependent cotranslational protein targeting to membrane’ is related to breast cancer. Since the orignal paper conclude that response of a certain drug for breast cancer is dependent on ER, the conclusion is supported.
| source | term_name | term_id | adjusted_p_value |
|---|---|---|---|
| GO:BP | SRP-dependent cotranslational protein targeting to membrane | GO:0006614 | 0 |
| GO:BP | viral transcription | GO:0019083 | 0 |
| GO:BP | cotranslational protein targeting to membrane | GO:0006613 | 0 |
| GO:BP | viral gene expression | GO:0019080 | 0 |
| GO:BP | viral process | GO:0016032 | 0 |
| GO:BP | symbiotic process | GO:0044403 | 0 |
| GO:BP | protein localization to endoplasmic reticulum | GO:0070972 | 0 |
| GO:BP | nuclear-transcribed mRNA catabolic process, nonsense-mediated decay | GO:0000184 | 0 |
| GO:BP | protein targeting to ER | GO:0045047 | 0 |
| GO:BP | establishment of protein localization to endoplasmic reticulum | GO:0072599 | 0 |
Choose GSEA(version 4.0.3) preranked analysis(Subramanian et al. 2005) since here we are using a ranked gene list. Database used is from bader lab, publised April 1st, 2020(Merico et al. 2010). Gene set permutation is the default and only choice for preranked analysis in GSEA. As for minimum gene set size, default GSEA setting ,which is 15, remain unchanged. However, maximum size of gene set is reduced to 200 in order to reduce runtime. Number of permutation is 1000.
Upregulated correspond to upregualted in treated samples and downregulated correspond to downregulated in treated sample.
In upregulated category, top gene returned is SELENOCYSTEINE SYNTHESIS%REACTOME%R-HSA-2408557.2, pvalue = 0.000, ES = 0.85, NES = 3.01, FDR = 0.000.
In downregulated category, top gene returned is HALLMARK_ESTROGEN_RESPONSE_EARLY%MSIGDB_C2%HALLMARK_ESTROGEN_RESPONSE_EARLY, pvalue = 0.000, ES = -0.77, NES = -2.69, FDR = 0.000.
| NAME | GS.br..follow.link.to.MSigDB | SIZE | ES | NES | NOM.p.val | FDR.q.val | FWER.p.val | RANK.AT.MAX | LEADING.EDGE |
|---|---|---|---|---|---|---|---|---|---|
| VIRAL MRNA TRANSLATION%REACTOME%R-HSA-192823.3 | VIRAL MRNA TRANSLATION%REACTOME%R-HSA-192823.3 | 84 | 0.8643032 | 3.007869 | 0 | 0 | 0 | 1179 | tags=74%, list=9%, signal=80% |
| PEPTIDE CHAIN ELONGATION%REACTOME%R-HSA-156902.2 | PEPTIDE CHAIN ELONGATION%REACTOME%R-HSA-156902.2 | 84 | 0.8640424 | 2.994553 | 0 | 0 | 0 | 1179 | tags=75%, list=9%, signal=82% |
| EUKARYOTIC TRANSLATION ELONGATION%REACTOME%R-HSA-156842.2 | EUKARYOTIC TRANSLATION ELONGATION%REACTOME%R-HSA-156842.2 | 88 | 0.8542727 | 2.989950 | 0 | 0 | 0 | 1179 | tags=72%, list=9%, signal=78% |
| SELENOCYSTEINE SYNTHESIS%REACTOME%R-HSA-2408557.2 | SELENOCYSTEINE SYNTHESIS%REACTOME%R-HSA-2408557.2 | 87 | 0.8538864 | 2.988714 | 0 | 0 | 0 | 1179 | tags=71%, list=9%, signal=78% |
| EUKARYOTIC TRANSLATION TERMINATION%REACTOME%R-HSA-72764.4 | EUKARYOTIC TRANSLATION TERMINATION%REACTOME%R-HSA-72764.4 | 88 | 0.8575868 | 2.967159 | 0 | 0 | 0 | 1179 | tags=70%, list=9%, signal=77% |
| CYTOPLASMIC RIBOSOMAL PROTEINS%WIKIPATHWAYS_20200310%WP477%HOMO SAPIENS | CYTOPLASMIC RIBOSOMAL PROTEINS%WIKIPATHWAYS_20200310%WP477%HOMO SAPIENS | 84 | 0.8384490 | 2.946797 | 0 | 0 | 0 | 1179 | tags=70%, list=9%, signal=76% |
| FORMATION OF A POOL OF FREE 40S SUBUNITS%REACTOME DATABASE ID RELEASE 72%72689 | FORMATION OF A POOL OF FREE 40S SUBUNITS%REACTOME DATABASE ID RELEASE 72%72689 | 95 | 0.8257862 | 2.941885 | 0 | 0 | 0 | 1203 | tags=69%, list=9%, signal=76% |
| COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE%GOBP%GO:0006613 | COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE%GOBP%GO:0006613 | 92 | 0.8197061 | 2.934378 | 0 | 0 | 0 | 1179 | tags=66%, list=9%, signal=72% |
| SELENOAMINO ACID METABOLISM%REACTOME DATABASE ID RELEASE 72%2408522 | SELENOAMINO ACID METABOLISM%REACTOME DATABASE ID RELEASE 72%2408522 | 105 | 0.8209368 | 2.928979 | 0 | 0 | 0 | 1179 | tags=61%, list=9%, signal=66% |
| SRP-DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE%REACTOME%R-HSA-1799339.2 | SRP-DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE%REACTOME%R-HSA-1799339.2 | 106 | 0.8149124 | 2.921791 | 0 | 0 | 0 | 1179 | tags=63%, list=9%, signal=69% |
| NAME | GS.br..follow.link.to.MSigDB | SIZE | ES | NES | NOM.p.val | FDR.q.val | FWER.p.val | RANK.AT.MAX | LEADING.EDGE |
|---|---|---|---|---|---|---|---|---|---|
| HALLMARK_ESTROGEN_RESPONSE_EARLY%MSIGDB_C2%HALLMARK_ESTROGEN_RESPONSE_EARLY | HALLMARK_ESTROGEN_RESPONSE_EARLY%MSIGDB_C2%HALLMARK_ESTROGEN_RESPONSE_EARLY | 173 | -0.7737817 | -2.686789 | 0.0000000 | 0.0000000 | 0.000 | 1704 | tags=51%, list=13%, signal=58% |
| HALLMARK_ESTROGEN_RESPONSE_LATE%MSIGDB_C2%HALLMARK_ESTROGEN_RESPONSE_LATE | HALLMARK_ESTROGEN_RESPONSE_LATE%MSIGDB_C2%HALLMARK_ESTROGEN_RESPONSE_LATE | 157 | -0.6456295 | -2.241304 | 0.0000000 | 0.0000000 | 0.000 | 1053 | tags=31%, list=8%, signal=33% |
| PATHWAYS AFFECTED IN ADENOID CYSTIC CARCINOMA%WIKIPATHWAYS_20200310%WP3651%HOMO SAPIENS | PATHWAYS AFFECTED IN ADENOID CYSTIC CARCINOMA%WIKIPATHWAYS_20200310%WP3651%HOMO SAPIENS | 56 | -0.7533783 | -2.235558 | 0.0000000 | 0.0000000 | 0.000 | 1273 | tags=50%, list=9%, signal=55% |
| EYE MORPHOGENESIS%GOBP%GO:0048592 | EYE MORPHOGENESIS%GOBP%GO:0048592 | 56 | -0.6926590 | -2.060055 | 0.0000000 | 0.0038307 | 0.014 | 1269 | tags=25%, list=9%, signal=27% |
| VISUAL SYSTEM DEVELOPMENT%GOBP%GO:0150063 | VISUAL SYSTEM DEVELOPMENT%GOBP%GO:0150063 | 111 | -0.6208371 | -2.058356 | 0.0000000 | 0.0030645 | 0.014 | 2187 | tags=32%, list=16%, signal=38% |
| CAMERA-TYPE EYE DEVELOPMENT%GOBP%GO:0043010 | CAMERA-TYPE EYE DEVELOPMENT%GOBP%GO:0043010 | 81 | -0.6463426 | -2.053742 | 0.0000000 | 0.0025538 | 0.014 | 403 | tags=16%, list=3%, signal=16% |
| HISTONE MODIFICATIONS%WIKIPATHWAYS_20200310%WP2369%HOMO SAPIENS | HISTONE MODIFICATIONS%WIKIPATHWAYS_20200310%WP2369%HOMO SAPIENS | 39 | -0.7408906 | -2.038358 | 0.0000000 | 0.0029178 | 0.019 | 1004 | tags=36%, list=7%, signal=39% |
| EYE DEVELOPMENT%GOBP%GO:0001654 | EYE DEVELOPMENT%GOBP%GO:0001654 | 110 | -0.6154625 | -2.036570 | 0.0000000 | 0.0029324 | 0.022 | 2187 | tags=32%, list=16%, signal=38% |
| SENSORY ORGAN MORPHOGENESIS%GOBP%GO:0090596 | SENSORY ORGAN MORPHOGENESIS%GOBP%GO:0090596 | 81 | -0.6355954 | -2.026007 | 0.0000000 | 0.0038485 | 0.032 | 1269 | tags=22%, list=9%, signal=24% |
| SENSORY SYSTEM DEVELOPMENT%GOBP%GO:0048880 | SENSORY SYSTEM DEVELOPMENT%GOBP%GO:0048880 | 115 | -0.6104676 | -2.022075 | 0.0000000 | 0.0036647 | 0.034 | 2187 | tags=31%, list=16%, signal=37% |
| LXR-MEDIATED SIGNALING%REACTOME DATABASE ID RELEASE 72%9024446 | LXR-MEDIATED SIGNALING%REACTOME DATABASE ID RELEASE 72%9024446 | 36 | -0.7204237 | -1.989345 | 0.0000000 | 0.0087138 | 0.089 | 824 | tags=36%, list=6%, signal=38% |
| CAMERA-TYPE EYE MORPHOGENESIS%GOBP%GO:0048593 | CAMERA-TYPE EYE MORPHOGENESIS%GOBP%GO:0048593 | 41 | -0.7044234 | -1.976283 | 0.0017182 | 0.0113972 | 0.128 | 403 | tags=17%, list=3%, signal=18% |
| SENSORY ORGAN DEVELOPMENT%GOBP%GO:0007423 | SENSORY ORGAN DEVELOPMENT%GOBP%GO:0007423 | 147 | -0.5809889 | -1.973475 | 0.0000000 | 0.0111504 | 0.136 | 2187 | tags=29%, list=16%, signal=34% |
| HISTONE METHYLATION%GOBP%GO:0016571 | HISTONE METHYLATION%GOBP%GO:0016571 | 56 | -0.6673597 | -1.964474 | 0.0000000 | 0.0124588 | 0.159 | 848 | tags=29%, list=6%, signal=30% |
| PKMTS METHYLATE HISTONE LYSINES%REACTOME DATABASE ID RELEASE 72%3214841 | PKMTS METHYLATE HISTONE LYSINES%REACTOME DATABASE ID RELEASE 72%3214841 | 42 | -0.6924467 | -1.962205 | 0.0000000 | 0.0118301 | 0.162 | 1004 | tags=31%, list=7%, signal=33% |
| CELL-SUBSTRATE JUNCTION ASSEMBLY%GOBP%GO:0007044 | CELL-SUBSTRATE JUNCTION ASSEMBLY%GOBP%GO:0007044 | 26 | -0.7598497 | -1.954675 | 0.0017271 | 0.0134554 | 0.191 | 1049 | tags=38%, list=8%, signal=42% |
| REGULATION OF MECP2 EXPRESSION AND ACTIVITY%REACTOME%R-HSA-9022692.1 | REGULATION OF MECP2 EXPRESSION AND ACTIVITY%REACTOME%R-HSA-9022692.1 | 28 | -0.7356510 | -1.953026 | 0.0000000 | 0.0133243 | 0.200 | 1185 | tags=39%, list=9%, signal=43% |
| MUSCLE CELL DIFFERENTIATION%GOBP%GO:0042692 | MUSCLE CELL DIFFERENTIATION%GOBP%GO:0042692 | 84 | -0.6097430 | -1.945164 | 0.0000000 | 0.0145124 | 0.228 | 1138 | tags=25%, list=8%, signal=27% |
| CELLULAR GLUCOSE HOMEOSTASIS%GOBP%GO:0001678 | CELLULAR GLUCOSE HOMEOSTASIS%GOBP%GO:0001678 | 37 | -0.7022272 | -1.938401 | 0.0000000 | 0.0157346 | 0.256 | 2153 | tags=49%, list=16%, signal=58% |
| RETINA DEVELOPMENT IN CAMERA-TYPE EYE%GOBP%GO:0060041 | RETINA DEVELOPMENT IN CAMERA-TYPE EYE%GOBP%GO:0060041 | 40 | -0.6929891 | -1.932622 | 0.0000000 | 0.0167860 | 0.282 | 403 | tags=20%, list=3%, signal=21% |
Compare to result from thresholded over-representation analysis, there are several common terms.
Between GSEA upregulated result and ORA result:SRP-dependent cotranslational protein targeting to memebrane, cotranslational protein targeting to membrane, protein targeting to ER and establishment of protein localization to endoplasmic reticulum. Moreover, in ORA result, we have term ‘translational initiation’, while in upregualted GSEA result, we have ‘CAP-dependent translational initiation’ and ‘eukaryotic translation initiation’
No common terms in top 20 terms from downregulated GSEA and ORA result.
Also, we checked common gene related to both results. Number of common gene is given in venn diagram. Detailed list of common gene are listed.
This is a straight forward comparision.
| Upregulated | Upregulated cont | Upregulated cont | Upregulated cont | Upregulated cont | Downregulated |
|---|---|---|---|---|---|
| RPS18 | RPL30 | RPL3 | RPLP2 | RPS7 | SMARCA4 |
| RPL27 | RPL37A | RPL13A | RPL8 | RPS3A | EP300 |
| RPS15A | RPS21 | SRP9 | RPLP0 | NUP37 | CTBP1 |
| RPS14 | RPS11 | RPL38 | RPL13 | NUP54 | CARM1 |
| RPS3 | RPL35A | RPL18 | RPL28 | PSMC3 | CAV1 |
| RPL24 | RPL27A | RPL7A | RPL5 | ARL6IP1 | BCL2 |
| RPS12 | RPL10A | RPL36 | RPL32 | UBB | NOTCH1 |
| RPS13 | RPL12 | RPS8 | RPS15 | PSMB1 | CREBBP |
| RPS5 | RPSA | RPL35 | RPL23A | TCEB1 | SRCAP |
| RPL26 | RPS4X | RPS25 | RPL39 | PSMA3 | HIPK2 |
| RPS6 | RPS27A | RPS9 | RPS19 | RBX1 | SLC22A5 |
| RPS20 | RPS24 | RPL31 | RPL11 | PSMB3 | INHBB |
| RPS16 | RPL9 | RPL29 | UBA52 | EIF4A2 | ANK2 |
| RPS18 | RPL30 | RPL3 | RPLP2 | PSMA4 | SMARCA4 |
| RPL27 | RPL37A | RPL13A | RPL8 | PSMB4 | EP300 |
| RPS15A | RPS21 | SRP9 | RPLP0 | EIF3E | CTBP1 |
| RPS14 | RPS11 | RPL38 | RPL13 | CARM1 | |
| RPS3 | RPL35A | RPL18 | RPL28 | CAV1 | |
| RPL24 | RPL27A | RPL7A | RPL5 | BCL2 | |
| RPS12 | RPL10A | RPL36 | RPL32 | NOTCH1 | |
| RPS13 | RPL12 | RPS8 | RPS15 | CREBBP | |
| RPS5 | RPSA | RPL35 | RPL23A | SRCAP | |
| RPL26 | RPS4X | RPS25 | RPL39 | HIPK2 | |
| RPS6 | RPS27A | RPS9 | RPS19 | SLC22A5 | |
| RPS20 | RPS24 | RPL31 | RPL11 | INHBB | |
| RPS16 | RPL9 | RPL29 | UBA52 | ANK2 |
Though I can run GSEA successfully in docker container, for reasons I don’t know, when I tried to run docker by code in the docker container. It always returns
"Error in curl::curl_fetch_memory(url, handle = handle) :
Failed to connect to localhost port 1234: Connection refused"
So I decided to create the html notebook for cytoscape pipeline outside the container, then merge the resulstant image file directly into the final hrml report. The cytoscape pipeline file are also submitted, named EM pipeline.Rmd. Since the GSEA result created by running code above in Docker container are causing problem becasue of in-Docker file path, I ran the code chunk for GSEA outside of docker first and ran cytoscape use this GSEA result. I manually set the path to GSEA result.
This enrichment map is created with p-value cutoff = 0.005 and FDR q-value cutoff=0.005. This q-value is selected to reduce size of network
This enrichment map includes 224 nodes and 4610 edges. 24 of 356 nodes are isolated nodes.
Red node represent ,and blue nodes represents.
Used autoannotation for this step. Default parameter were chosen. Detailed parameter information are listed below:
Additional Information about the EnrichmentMap:
Size of node correspond to size of geneset. Red nodes correspond to upregulated, blue nodes correspond to downregulated. Labels of node are geneset description. Thickness of the edge correspond to similarity coefficient. The more genes two nodes share in common, the thicker the edge.
Similar to result from assignment 2, GSEA and enrichment map result support the original paper.
Recall the original paper(Petrossian et al. 2018), it says that the ER treatment is important for increased CDK4/6 response in HR+ breast cancer. Refer to our enrichment map, proteasome degradation is an important feature that related to upregulated enrichment result. Proteasome degradation leads to inhibited proteasome activity, which could increase tumour killing by decrease concenration of P-glyco-protein in membrane cells.(Orlowski and Dees 2002) In addition, mitocchondrial translational elongation is also upregulated term. Since mitochodrial dysfunction is associated with increased aggressiveness of breast cancer(Lunetti et al. 2019), upregulated translational elongation is supposed to be able to help breast cancer patient. Moreover, estrogen response, which is positively related to cancer cell growth(Petrossian et al. 2018), is downregulated.
Therefore, the conclusion of the original paper is supported by our analysis.
Here I chose proteasome degradation. There are 2 reasons for chosing this pathway. First, this is a major pathway that clearly related to increased tumour killing. Second, size of its geneset is relatively large.
Here I use GeneMania, chose automatic network weighting and 0 max resultant gene, as default. Predicted link is the one weight the most in the resultant network, but I chose not to show this type of link since I’m more interested in confirmed relationships. In addition, I removed co-localization links.
legend
I examined the gene node with highest rank, which are UBA7, IFNG, HLA-G, and PSMD5. UBA7 can perform as a marker for breast cancer patient since expression of UBA7 will be significantly reduced in breast cancer(Lin et al. 2020), however, here in our sample, expression of UBA7 is increased.As for IFNG, IFNG helps cancer therapy by facilitate tumor clearance and tumor escape (Ni and Lu 2018), therefore also has a positive effect in breast cancer therapies. HLA-G is rarely found in breast cancer tissues, but here it is highly upregulated(Palmisano et al. 2002), which indicate the experiment treatment is related to positive effect on breast cancer.At, last PSMD5 encodes essential 26s subunit of proteasome, since we observed a highly upregulated score for proteasome degradation, it is reasonable that there is more proteasome synthesised. So result of examination on proteasome degradation is consistent with what I found on enrichment map.
Lin, Meng, Yanqing Li, Shanshan Qin, Yan Jiao, and Fang Hua. 2020. “Ubiquitin-Like Modifier-Activating Enzyme 7 as a Marker for the Diagnosis and Prognosis of Breast Cancer.” Oncology Letters 19 (4). Spandidos Publications: 2773–84.
Lunetti, Paola, Mariangela Di Giacomo, Daniele Vergara, Stefania De Domenico, Michele Maffia, Vincenzo Zara, Loredana Capobianco, and Alessandra Ferramosca. 2019. “Metabolic Reprogramming in Breast Cancer Results in Distinct Mitochondrial Bioenergetics Between Luminal and Basal Subtypes.” The FEBS Journal 286 (4). Wiley Online Library: 688–709.
Merico, Daniele, Ruth Isserlin, Oliver Stueker, Andrew Emili, and Gary D Bader. 2010. “Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation.” PloS One 5 (11). Public Library of Science.
Ni, Ling, and Jian Lu. 2018. “Interferon Gamma in Cancer Immunotherapy.” Cancer Medicine 7 (9). Wiley Online Library: 4509–16.
Orlowski, Robert Z, and E Claire Dees. 2002. “The Role of the Ubiquitination-Proteasome Pathway in Breast Cancer: Applying Drugs That Affect the Ubiquitin-Proteasome Pathway to the Therapy of Breast Cancer.” Breast Cancer Research 5 (1). Springer: 1.
Palmisano, Giulio Lelio, Maria Pia Pistillo, Paolo Fardin, Paolo Capanni, Guido Nicolò, Sandra Salvi, Bruno Spina, Gennaro Pasciucco, and Giovanni Battista Ferrara. 2002. “Analysis of Hla-G Expression in Breast Cancer Tissues.” Human Immunology 63 (11). Elsevier: 969–76.
Petrossian, Karineh, Noriko Kanaya, Chiao Lo, Pei-Yin Hsu, Duc Nguyen, Lixin Yang, Lu Yang, et al. 2018. “ER\(\alpha\)-Mediated Cell Cycle Progression Is an Important Requisite for Cdk4/6 Inhibitor Response in Hr+ Breast Cancer.” Oncotarget 9 (45). Impact Journals, LLC: 27736.
Subramanian, Aravind, Pablo Tamayo, Vamsi K Mootha, Sayan Mukherjee, Benjamin L Ebert, Michael A Gillette, Amanda Paulovich, et al. 2005. “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.” Proceedings of the National Academy of Sciences 102 (43). National Acad Sciences: 15545–50.